Dose Escalation and Co-therapy Intensification Between Etanercept, Adalimumab, and Infliximab: The CADURA Study
Bibliographic record
Abstract
OBJECTIVE: To compare anti-TNF dose escalation, DMARD and/or glucocorticoid intensification, switches to another biologic, and drug and drug-related costs over 12 and 18 months for rheumatoid arthritis (RA) patients initiating etanercept (ETN), adalimumab (ADA), or infliximab (IFX) in routine clinical practice across Canada. METHODS: A retrospective chart review of biologic-naïve adult RA patients newly initiating ADA, ETN, or IFX between January 01, 2006 and December 31, 2012 from 11 practices across Canada. RESULTS: There were 314 patients in the 12-month analysis and 217 in the 18-month analysis. No dose escalation occurred with ETN over 12 and 18 months versus 38% and 32% for IFX (p<0.001) and 2% and 2% for ADA (p=0.199, p=0.218). Over 18 months, dose escalation and/or DMARD and/or glucocorticoid intensification was less frequent among ETN (16%) versus IFX (44%, p=0.005) and ADA (34%, p=0.004). By 18 months, 22% of patients initiating ADA had switched to another biologic compared with 6% of ETN patients (p=0.001).Patients initiating ETN had lower total (drug and drug-related) costs over 12 and 18 months compared to IFX, and no difference compared to ADA when adjusted for potential confounders. Patients with dose escalation had higher costs compared to those with no dose escalation. CONCLUSION: Physicians were more likely to escalate the dose of IFX, but optimize co-therapy with ADA and ETN. ETN patients had no dose escalation and were less likely to have DMARD and/or glucocorticoid intensification than ADA patients. ETN-treated patients had lower costs compared to IFX patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".